U.S. patent application number 14/072851 was filed with the patent office on 2015-05-07 for detecting incorrectly placed access points.
This patent application is currently assigned to Cisco Technology, Inc.. The applicant listed for this patent is Cisco Technology, Inc.. Invention is credited to Santosh Ghanshyam Pandey.
Application Number | 20150126215 14/072851 |
Document ID | / |
Family ID | 51952019 |
Filed Date | 2015-05-07 |
United States Patent
Application |
20150126215 |
Kind Code |
A1 |
Pandey; Santosh Ghanshyam |
May 7, 2015 |
DETECTING INCORRECTLY PLACED ACCESS POINTS
Abstract
Embodiments provide techniques for detecting access points on a
position map, particularly incorrectly placed access points. For
each access point in a plurality of access points, a subset of the
plurality of access points that neighbor the access point are
identified. Embodiments estimate a location of the access point,
based on a respective indication of signal strength from each
neighboring access point in the subset of access points and a
respective position of each of the neighboring access points in
position map. A difference between a recorded position of the
access point in the position map and the estimated location of the
access point is calculated. Embodiments then determine that the
position within the position map for a first one of the plurality
of access points is incorrect, based on the determined difference
for the first access point.
Inventors: |
Pandey; Santosh Ghanshyam;
(Newark, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cisco Technology, Inc. |
San Jose |
CA |
US |
|
|
Assignee: |
Cisco Technology, Inc.
San Jose
CA
|
Family ID: |
51952019 |
Appl. No.: |
14/072851 |
Filed: |
November 6, 2013 |
Current U.S.
Class: |
455/456.1 |
Current CPC
Class: |
G01S 5/021 20130101;
G01S 5/0252 20130101; G01S 5/0242 20130101; H04W 64/003
20130101 |
Class at
Publication: |
455/456.1 |
International
Class: |
G01S 5/02 20060101
G01S005/02; H04W 64/00 20060101 H04W064/00 |
Claims
1. A method, comprising: for each access point in a plurality of
access points, and by operation of one or more computer processors:
identifying a subset of the plurality of access points that
neighbor the access point; estimating a location of the access
point, based on a respective indication of signal strength from
each neighboring access point in the subset of access points and a
respective position of each of the neighboring access points in a
position map; and calculating a difference between a recorded
position of the access point in the position map and the estimated
location of the access point; and determining the position within
the position map for a first one of the plurality of access points
is likely to be incorrect, based on the determined difference for
the first access point.
2. The method of claim 1, wherein the indication of signal strength
comprises at least one of a received signal strength indication
(RSSI) measure, a time of arrival measurement, a one way transit
time measurement and a round trip time measurement.
3. The method of claim 2, wherein estimating a location of the
access point is further based indications of signal strength from
each neighboring access point, wherein the indications of signal
strength correspond to different signal bands.
4. The method of claim 1, wherein the position map specifies a
respective position for each of the plurality of access points
within a physical environment.
5. The method of claim 4, wherein the position is specified using
coordinates specifying a position within the physical
environment.
6. The method of claim 1, further comprising: upon determining the
position within the position map for the first access point is
likely to be incorrect, generating a notification specifying the
first access point.
7. The method of claim 1, wherein determining the position within
the position map for the first access point is likely to be
incorrect further comprises: selecting the first access point as
the access point from the plurality of access points having a
greatest calculated difference.
8. The method of claim 1, wherein determining the position within
the position map for the first access point is likely to be
incorrect further comprises: determining that the calculated
difference for the first access exceeds a predefined threshold.
9. A method, comprising: generating, by operation of one or more
computer processors, a set of signal strength and distance data,
comprising, for each access point in a plurality of access points:
identifying a subset of the plurality of access points that
neighbor the access point; collecting indications of signal
strength for each of the access points in the subset, relative to
the access point; calculate a distance between the access point and
each of the access points in the subset, based on the position map;
generating a line of best fit to the set of signal strength and
distance data for each of the plurality of access points;
determining a likelihood value for each of the plurality of access
points, based on the line of best fit, the indications of signal
strength and the calculated distance for the access point; and
determining the position within the position map for a first one of
the plurality of access points is likely to be incorrect, based on
the determined likelihoods for each of the plurality of access
points.
10. The method of claim 9, wherein generating the line of best fit
to the set of signal strength and distance data further comprises:
fitting a path loss model to the set of signal strength and
distance data.
11. The method of claim 9, wherein determining the likelihood for
each of the plurality of access points further comprises:
determining an average negative log likelihood for each of the
plurality of access points, and wherein determining the position
within the position map for a first one of the plurality of access
points is likely to be incorrect further comprises: selecting the
access point having the greatest average negative log likelihood as
the first access point.
12. The method of claim 9, further comprising: excluding all
indications of signal strength and distance information relating to
neighbors of the access point from the set of signal strength and
distance data, wherein generating the line of best fit to the set
of signal strength and distance data for each of the plurality of
access points is based on the set of signal strength and distance
data with the neighboring information excluded.
13. The method of claim 9, wherein the indication of signal
strength comprises at least one of a received signal strength
indication (RSSI) measure, a time of arrival measurement, a one way
transit time measurement and a round trip time measurement.
14. The method of claim 13 wherein collecting indications of signal
strength for each of the access points in the subset, relative to
the access point, comprises: collecting indications of signal
strength across multiple different signal bands.
15. The method of claim 9, wherein the position map specifies a
respective position for each of the plurality of access points
within a physical environment.
16. The method of claim 15, wherein the position is specified using
coordinates specifying a position within the physical
environment.
17. The method of claim 9, further comprising: upon determining the
position within the position map for the first access point is
likely to be incorrect, generating a notification specifying at
least the first access point.
18. A method, comprising: receiving a position map specifying a
respective position for each of a plurality of access points within
a physical environment; collecting indications of signal strength
from each of the plurality of access points; and analyzing, by
operation of one or more computer processors, the indications of
signal strength and the position map to determine that a position
of a first one of the plurality of access points within the
position map is likely to be incorrect, in that the position of the
first access point within the position map does not correspond to a
position of the first access point within the physical
environment.
19. The method of claim 18, further comprising: upon determining
that the position within the position map for the first access
point is likely to be incorrect, generating a notification
specifying at least the first access point.
20. The method of claim 18, wherein analyzing the indications of
signal strength and the position map further comprises: plotting a
line of best fit using a path loss model; and determining a
likelihood value for each of the plurality of access points, based
on the line of best fit and the indication of signal strength and a
calculated distance for the access point.
Description
TECHNICAL FIELD
[0001] Embodiments presented in this disclosure generally relate to
floor map verification, and more specifically to detecting
misplaced access points within a floor map based on measures of
signal strength between the access points.
BACKGROUND
[0002] Wireless networks such as wireless local area networks
(WLANs) are quickly becoming pervasive, and WLANs that conform to
the IEEE 802.11 standard are particularly ubiquitous. A WLAN can be
made up of one or more wireless access points. A wireless access
point is generally a device that enables wired communication
devices (e.g., network devices) to connect to and to transmit data
through a wireless network using wireless technologies (e.g.,
Wi-Fi, Bluetooth, or related standards). For example, an access
point could connect to a network device (e.g., an edge device) and
could relay data between wireless client devices (e.g., personal
computers, printers, mobile devices, etc.) and the network device.
The access point could also be combined with a wireless network
device, such as in a wireless router.
[0003] Generally, a given access point will have a fixed area in
which it can provide an acceptable signal strength. In order to
create a WLAN spanning a larger area, network engineers will
oftentimes use multiple access points in a wired network in order
to provide wireless access to client devices within the larger
area. The access points within the WLAN may work in conjunction to
provide network access for the client devices, and may be managed
by a WLAN controller. The WLAN controller generally performs
management functions for the plurality of access points within the
wireless network, e.g., automatic adjustments to radio frequency
(RF) power, channels, authentication, and/or security associated
with the access points.
[0004] In many cases, wireless networks are deployed in areas too
large to be covered by a single wireless access point. That is, a
single wireless access point is generally capable of providing
network access for a fixed area. As the area covered by the
wireless network increases, so does the number of access points and
the difficulty of arranging and managing the access points. To
assist network engineers in managing such networks, a position map
may be created, e.g., using blueprints or other drawings of a
facility. Such a position map may specify, for example, a physical
position of each of the access points within the physical
environment. A network engineer could then use such a map for RF
coverage optimization functions, such as defining optimal channels
and power level selection for each of the access points, as well as
WLAN location-based services such as client device tracking. The
position map may be created manually by a network administrator
importing a set of floor plans and manually selecting access point
locations within the map corresponding to the physical access
points within the physical environment. However, such a manual
process is oftentimes prone to human errors, and once an incorrect
position is assigned to an access point within the position map, it
can be a challenging and time consuming task to identify and
correct the error.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] So that the manner in which the above-recited features of
the present disclosure can be understood in detail, a more
particular description of the disclosure, briefly summarized above,
may be had by reference to embodiments, some of which are
illustrated in the appended drawings. It is to be noted, however,
that the appended drawings illustrate only typical embodiments of
this disclosure and are therefore not to be considered limiting of
its scope, for the disclosure may admit to other equally effective
embodiments.
[0006] FIG. 1 is a diagram illustrating an interaction between a
position map analysis component and a physical environment,
according to one embodiment described herein.
[0007] FIG. 2 illustrates access point positions within a physical
environment, according to one embodiment described herein.
[0008] FIG. 3 is a flow diagram illustrating a method for
identifying an access point having an incorrect position within a
position map, according to one embodiment described herein.
[0009] FIG. 4 is a flow diagram illustrating a method for
calculating distances between access points within a position map
based on measures of signal strength, according to one embodiment
described herein.
[0010] FIG. 5 is a flow diagram illustrating a method for
identifying an access point having an incorrect position within a
position map, according to one embodiment described herein.
[0011] FIG. 6 is a block diagram illustrating a system configured
with a position map analysis component, according to one embodiment
described herein.
DESCRIPTION OF EXAMPLE EMBODIMENTS
Overview
[0012] One embodiment provides a method for detecting an
incorrectly placed access point on a position map. For each access
point in a plurality of access points, a subset of the plurality of
access points that neighbor the access point are identified.
Embodiments estimate a location of the access point, based on a
respective indication of signal strength from each neighboring
access point in the subset of access points and a respective
position of each of the neighboring access points in position map.
A difference between a recorded position of the access point in the
position map and the estimated location of the access point is
calculated. Embodiments then determine that the position within the
position map for a first one of the plurality of access points is
incorrect, based on the determined difference for the first access
point.
[0013] Another embodiment provides a method that includes
generating a set of signal strength and distance data for each
access point in a plurality of access points. Generating the set of
signal strength and distance data includes identifying a subset of
the plurality of access points that neighbor the access point.
Additionally, the generating includes collecting indications of
signal strength for each of the access points in the subset,
relative to the access point, and calculating a distance between
the access point and each of the access points in the subset, based
on the position map. The method also includes generating a line of
best fit to the set of signal strength and distance data for each
of the plurality of access points, and further includes determining
a likelihood value for each of the plurality of access points,
based on the line of best fit and the indication of signal strength
and calculated distance for the access point. Moreover, the method
includes determining that the position within the position map for
a first one of the plurality of access points is likely to be
incorrect, based on the determined likelihoods for each of the
plurality of access points.
Example Embodiments
[0014] Embodiments generally provide techniques for identifying
incorrectly placed access points within a position map. As used
herein, a "position map" is a data structure that identifies a
position for each of a plurality of access points within a physical
environment. Such positions may be specified, for example, using
coordinates that identify a particular position within the physical
environment. As used herein, a position of an access point within a
position map is said to be "incorrect" when the access point's
position within the position map does not correspond to the access
point's position within the physical environment. For example, a
position map could specify coordinates indicating that a particular
access point is located within a conference room of a physical
environment, while the particular access point could actually be
located in a room adjacent to the conference room. In such an
example, the particular access point's position within the position
map would be incorrect, as the position within the position map
does not match the actual position of the physical access
point.
[0015] FIG. 1 is a diagram illustrating an interaction between a
position map analysis component and a physical environment,
according to one embodiment described herein. As shown, the
illustration 100 includes a position map analysis system 110 and a
physical environment 130. The position map analysis system 110
includes a position map analysis component 115 and a position map
data structure 120. The physical environment 130 represents a floor
of a physical building having multiple access point devices 135
placed throughout.
[0016] Generally, the position map analysis component 115 is
configured to detect incorrectly placed access points within the
position map data structure 120 (i.e., access points whose position
within the position map 120 does not match the position of the
physical access point 135 within the physical environment 130). For
example, the position map analysis component 115 could, for each
access point in a plurality of access points, identify a subset of
the plurality of access points that neighbor the access point.
Generally, access points within a position map can be classified as
"neighbors" when the access points are within the radio frequency
(RF range of one another.
[0017] In a particular embodiment, relationships amongst the access
points within the position map can be classified as one of a
parent, a child and a neighbor. For instance, a parent access point
could be defined as an access point that offers a "best route" to a
root access point, where the parent can be the root access point
itself or a mesh access point. The "best route" can be determined
based on an ease value, which is calculated using a signal-to-noise
ratio and link hop value of each neighboring access point. The
access point having a higher ease value can be selected for the
best route. Additionally, a child access point could select the
parent access point as the best route back to the root access
point. In such an embodiment, a neighbor access point could be an
access point within RF range of another access point, but that is
not selected as either the parent or the child, due to having an
ease value lower than the parent's ease value.
[0018] Once the neighboring access points are identified, the
position map analysis component 115 could estimate a location of
the access point, based on a respective indication of signal
strength from each neighboring access point in the subset of access
points and a respective position of each of the neighboring access
points in position map. The indication of signal strength could be,
for example, a received signal strength indication (RSSI) measure.
Other examples of the indication of signal strength include,
without limitation, a time of arrival measurement, a one way
transit time measurement and a round trip time measurement. More
generally, any metric that scales with the physical distance
between access points can be used, consistent with the
functionality described herein.
[0019] The position map analysis component 115 could estimate the
access point's position by estimating a distance between the access
point and each of its neighbors, based on the indication of signal
strength between the access point and each neighbor. For example, a
weaker indication of signal strength can represent a further
distance between the two access points, and a stronger indication
of signal strength can represent a shorter distance between the two
access points. The position map analysis component 115 could then
estimate the access point's position, based on the positions of the
neighboring access points within the position map data structure
120.
[0020] The position map analysis component 115 could then calculate
a difference between a recorded position of the access point in the
position map 120 and the estimated location of the access point.
The position map analysis component 115 could then determine that
the position within the position map for a particular one of the
plurality of access points is incorrect, based on the calculated
differences. Here, a greater calculated difference between the
position of the access point within the position map 120 and the
estimated position may indicate an increased likelihood that the
position of the access point within the position map 120 is
incorrect. As such, the position map analysis component 115 could
select the access point having the greatest calculated difference
as an access point having an incorrect position within the position
map 120.
[0021] In one embodiment, the position map analysis component 115
is configured to calculate an error value for each of the access
points and to select the access point(s) having the greatest error
values as having an incorrect position(s) within the position map
120. For example, the position map analysis component 115 could
calculate an error for each access point by determining the
difference between the access point's position within the position
map and the estimated position for the access point. Additionally,
since the incorrectly placed access point within the position map
can potentially be one of the access point's neighbors (which could
also have an effect on the estimated position for the access
point), the position map analysis component 115 could add the error
values for each of the neighboring access points to the access
point's error value to generate a total error value for the access
point. The position map analysis component 115 could generate such
a total error value for each of the access points, and could select
the access point(s) having the greatest total error value as the
incorrectly placed access point(s) within the position map. In one
embodiment, the position map analysis component 115 is configured
to weight each access point's own error value more than the
neighboring access points' error values when calculating the total
error values (e.g., by applying a 2.times. multiplier to the access
point's own error value).
[0022] An example will now be discussed with respect to FIG. 2,
which illustrates access point positions within a position map and
a physical environment, according to one embodiment described
herein. As shown, the illustration 200 includes an access point 205
having coordinates 210, an access point 215 having coordinates 220,
an access point 225 having coordinates 230, an access point 235
having coordinates 240, an access point 245 having coordinates 250
and an access point 255 having coordinates 260. Additionally, the
illustration 200 depicts a physical access point 225' having actual
coordinates 230'. Here, the physical access point 225' corresponds
to the access point 225 within the position map, and the access
point 225 would be considered "incorrect" within the position map,
as the coordinates 230 do not match the coordinates 230' of the
physical access point 225' within the physical environment.
[0023] In one embodiment, the position map analysis component 115
is configured to plot a line of best fit to a set of signal
strength and distance data, in order to detect an access point
having an incorrect position within a position map data structure.
For example, the position map analysis component 115 could generate
a set of signal strength and distance data, by, for each access
point in a plurality of access points, identifying a subset of the
plurality of access points that neighbor the access point.
Additionally, in generating the set of data, the position map
analysis component 115 could collect indications of signal strength
for each of the access points in the subset, relative to the access
point. As discussed above, the indication of signal strength can be
a measure of RSSI between the access points. In some embodiments,
the access points may already maintain signal strength information
for each of the neighboring access points, in which case the
position map analysis component 115 could simply access this
existing information. In particular embodiments, the indication of
signal strength can be another metric(s) that scales with the
physical distance between access points, such as a time of arrival,
a one-way transit time and a round-trip time.
[0024] The position map analysis component 115 could also calculate
a distance between the access point and each of the neighboring
access points, based on the positions of the access points within
the position map. Generally speaking, any technique for use in
calculating the distance between two points may be used, consistent
with the functionality described herein. As one example, in an
embodiment where positions are specified using coordinates, the
position map analysis component 115 could calculate the distance
using the formula:
Distance= {square root over
((x.sub.2-x.sub.1).sup.2+(y.sub.2-y.sub.1).sup.2)}{square root over
((x.sub.2-x.sub.1).sup.2+(y.sub.2-y.sub.1).sup.2)} Equation
1--Distance Calculation
[0025] Once the set of signal strength and distance data is
determined, the position map analysis component 115 could generate
a line of best fit to the set of signal strength and distance data
for each of the plurality of access points. For example, the
position map analysis component 115 could fit a path loss model to
the set of data for a particular access point in order to generate
the line of best fit. In doing so, the position map analysis
component 115 could exclude data from access points neighboring the
access point.
[0026] The position map analysis component 115 could then determine
a likelihood value for each of the access point's neighbor's
indications of signal strength (e.g., an RSSI measurement), based
on the line of best fit and the indication of signal strength and
calculated distance for the access point. For instance, the
position map analysis component 115 could calculate a negative log
likelihood for each RSSI measurement, using the following
equation:
Negative Log Likelihood of Signal Strength Indication -
log_likelihood ij = ( rssi ij - rssi_bestfit ij ) 2 2 * standard
deviation 2 + log ( 1 standard deviation * 2 .pi. ) Equation 2
##EQU00001##
[0027] Here, the rssi_bestfit.sub.ij represents the RSSI
measurement from the line of best fit at the distance
distance.sub.ij, and the distance.sub.ij represents the calculated
distance between the two access points corresponding to the RSSI
measurement being considered. The position map analysis component
115 could then determine the negative log likelihood measurement
for each access point neighboring the access point in question, and
could then calculate an average negative log likelihood measure for
the access point using the following equation:
Average Negative Log Likelihood average_negative _log _likelihood i
= ( j = 1 n - log_likelihood ij ) / n Equation 3 ##EQU00002##
[0028] Here, n represents the number of neighboring access points
for an access point i. As shown, Equation 3 states that the average
negative log likelihood for the access point i can be determined by
calculating the sum of negative log likelihood values for each
access point neighboring access point i, and by then dividing by
the number of neighboring access points for access point i (i.e.,
n). The position map analysis component 115 could then calculate an
average negative log likelihood value for each access point in the
position map.
[0029] Once the average negative log likelihood values are
determined, the position map analysis component 115 could select
the access point having the greatest average negative log
likelihood value as the access point incorrectly positioned within
the position map. For example, the position map analysis component
115 could sort the average negative log likelihood values
calculated for each of the access points, and could select the
access point(s) having the highest average negative log likelihood
values as being the access point that is most likely incorrectly
placed within the position map.
[0030] Of note, a selection of an access point by the position map
analysis component 115 may not indicate that the access point is in
fact incorrectly positioned within the position map, but rather
indicates that the selected access point is likely to be
incorrectly positioned within the position map. A network
administrator could then use such information to, for example,
verify the positions of only a subset of the access points within
the position map (i.e., only the access points identified by the
position map analysis component 115 as likely to be incorrectly
positioned).
[0031] In one embodiment, each of the plurality of access points is
a dual band access point that is capable of transmitting
simultaneously on multiple different bands (e.g., a dual band
device capable of operating simultaneously on the 5 GHz band of
802.11a and the 2.4 GHz band used by 802.11b, 802.11g and 802.11n).
In such an embodiment, the position map analysis component 115
could collect indications of signal strength between the access
points on each of the different bands, for use in determining one
of the access points that is incorrectly positioned within the
position map data structure. By considering the indications of
signal strength across the multiple bands, the position map
analysis component 115 can consider additional data points, which
can lead to more accurate determinations of incorrectly placed
access points within the position map.
[0032] FIG. 3 is a flow diagram illustrating a method for
identifying an access point having an incorrect position within a
position map, according to one embodiment described herein. As
shown, the method 300 begins at block 310, where a position map
analysis component 115 receives a position map specifying a
position of each of a plurality of access points. Such a position
could be specified, for example, using coordinates that uniquely
identify a location with a physical environment. The method 300
then enters a loop for each of the plurality of access points
(block 315), where the position map analysis component 115
identifies a subset of the plurality of access points that neighbor
the access point in question (block 320).
[0033] The position map analysis component 115 then determines an
indication of signal strength for each access point in the subset,
relative to the access point in question (block 325). For example,
the indication of signal strength could be a measure of RSSI
between the neighboring access point and the access point in
question. In one embodiment, where the access points are capable of
simultaneously operating on multiple different bands (e.g.,
dual-band access points), the position map analysis component 115
can determine multiple indications of signal strength between each
pair of access points (i.e., one for each band).
[0034] Additionally, the position map analysis component 115
determines a position for each of the neighboring access points
using the position map (block 330), and estimates a position of the
access point in question, based on the indications of signal
strength between the access point and the neighboring access
points, as well as the determined positions of the neighboring
access points (block 335). Once the estimated position for the
access point is determined, the position map analysis component 115
calculates a difference between the estimated position and the
position of the access point within the position map (block
340).
[0035] At block 345, if there are more access points to be
considered, the method 300 returns to block 315. Otherwise, the
method 300 advances to block 350, where the position map analysis
component 115 determines that the position within the position map
for at least one of the access points is likely to be incorrect,
based on the calculated differences, and the method 300 ends. For
example, the position map analysis component 115 could sort the
calculated differences and could select the access point having the
greatest calculated difference as the access point most likely to
have an incorrect position within the position map.
[0036] As discussed above, in one embodiment the position map
analysis component 115 is configured to calculate a total error
value for each of the access points, and to select the access point
that is most likely to be incorrectly positioned within the
position map based on the total error values. For instance, the
position map analysis component 115 could calculate a given access
point's total error by applying a weight value to the access
point's calculated difference value (i.e., the difference between
the access point's estimated position and the access point's
position within the position map), and could then add this value to
the sum of all the calculated difference values for each of the
access points neighbors. The position map analysis component 115
could then select the access point having the greatest total error
value, as the access point most likely to be incorrectly positioned
within the position map.
[0037] An alternate embodiment of identifying access points that
are likely to be incorrectly positioned within a position map is
described in FIGS. 4 and 5. FIG. 4 is a flow diagram illustrating a
method for calculating distances between access points within a
position map based on measures of signal strength, according to one
embodiment described herein. As shown, the method 400 begins at
block 410, where the position map analysis component 115 receives a
position map specifying a position of each of a plurality of access
points. The position map analysis component 115 then performs
blocks 420, 425 and 430 for each of the plurality of access points,
as indicated by block 415. That is, for each access point, the
position map analysis component 115 identifies a subset of the
plurality of access points that neighbor the access point in
question (block 420), and collects indications of signal strength
(e.g., RSSI measurements) for each of the neighboring access points
(block 425). The position map analysis component 115 also
calculates a distance between the access point in question and each
of its neighbors, based on the positions of the access points
within the position map (block 430), and the method 400 ends.
[0038] FIG. 5 is a flow diagram illustrating a method for
identifying an access point having an incorrect position within a
position map, according to one embodiment described herein. As
shown, the method 500 begins at block 510, where the position map
analysis component 115 collects signal strength and distance
information for each access point in the position map. For example,
the position map analysis component 115 could collect such
information using the method 400 described above.
[0039] The method 500 then enters a loop from blocks 515 to 540,
where the position map analysis component 115 performs blocks 520,
525, 530 and 535 for each access point in the position map. As
shown, the position map analysis component 115 excludes all
neighboring access point signal strength and distance information
from the data set, for purposes of fitting a path loss model to the
data (block 520). The position map analysis component 115 then fits
a path loss model using the remaining signal strength and distance
information, in order to determine a line of best fit for the data
set (block 525). The position map analysis component 115 then, for
each neighbor to the access point in question, determines a
negative log likelihood of the signal strength and distance
measurement, based on the line of best fit (block 530). For
example, the position map analysis component 115 could calculate
the negative log likelihood using Equation 2 discussed above.
[0040] Once the negative log likelihood is calculated for each
neighboring access point, the position map analysis component 115
calculates an average negative log likelihood for the access point
by averaging all of the neighboring negative log likelihood values
(block 535). For example, the position map analysis component 115
could calculate the average value using Equation 3 discussed above.
Once the position map analysis component 115 has calculated the
average negative log likelihood value for each of the access
points, the position map analysis component 115 determines that the
access point having the greatest average log likelihood value is
the access point most likely to be incorrectly positioned within
the position map (block 545), and the method 500 ends. For example,
the position map analysis component 115 could sort all of the
average negative log likelihood values, and could select the
greatest value as identifying the access point most likely to be
incorrectly positioned within the position map.
[0041] The position map analysis component 115 could then, for
example, generate a notification (e.g., within a graphical user
interface) identifying the access point as likely to be incorrectly
positioned. A network engineer could then verify the access point's
position within the physical environment. By calling specific
access points to the network engineer's attention that are likely
to be incorrectly positioned, the position map analysis component
115 is able to drastically reduce the network engineer's workload,
as the network engineer need only verify the positions of the
designated access point(s), as opposed to the positions of all of
the access points within the wireless network.
[0042] FIG. 6 is a block diagram illustrating a system configured
with a position map analysis component, according to one embodiment
described herein. The position map analysis system 600 includes a
processor 615, storage 620, memory 625, input/output (I/O) devices
630 and a network adapter 635. The processor 615 may be any
processing element capable of performing the functions described
herein. The processor 615 represents single processor, multiple
processors, a processor with multiple cores, and combinations
thereof. Storage 620 represents any non-volatile memory (e.g., a
disk drive) on the position map analysis system 600 or accessible
by the position map analysis system 600.
[0043] The memory 625 may be either volatile or non-volatile memory
and include, RAM, flash, cache, disk drives and the like. Although
shown as a single entity, the memory 625 may be divided into
different memory storage elements such as RAM and one or more hard
disk drives. Here, the memory 625 includes a position map analysis
component 115 and a position map data structure 120. The network
adapter 635 facilitates communication between the position map
analysis system 600 and a network. Here, the network is
representative of any data communications network on which the
position map analysis system could transmit data, including wired
networks, wireless networks, etc. Examples of the network include a
local area network, the Internet, a Bluetooth.RTM. communication
link, and so on.
[0044] As discussed above, the position map analysis component 115
is configured to, for each access point in a plurality of access
points in the position map data structure 120, identify a subset of
the plurality of access points that neighbor the access point.
Additionally, the position map analysis component 115 can
estimating a location of each of the access points, based on a
respective indication of signal strength from each neighboring
access point in the subset of access points and a respective
position of each of the neighboring access points in position map.
The position map analysis component 115 could then calculate a
difference between a recorded position of the access point in the
position map and the estimated location of the access point, and
could determine that the position within the position map for a
first one of the plurality of access points is likely to be
incorrect, based on the determined difference for the first access
point.
[0045] Additionally, it is specifically contemplated that
embodiments may be provided to end users through a cloud computing
infrastructure. Cloud computing generally refers to the provision
of scalable computing resources as a service over a network. More
formally, cloud computing may be defined as a computing capability
that provides an abstraction between the computing resource and its
underlying technical architecture (e.g., servers, storage,
networks), enabling convenient, on-demand network access to a
shared pool of configurable computing resources that can be rapidly
provisioned and released with minimal management effort or service
provider interaction. Thus, cloud computing allows a user to access
virtual computing resources (e.g., storage, data, applications, and
even complete virtualized computing systems) in "the cloud,"
without regard for the underlying physical systems (or locations of
those systems) used to provide the computing resources.
[0046] Cloud computing resources may be provided to a user on a
pay-per-use basis, where users are charged only for the computing
resources actually used (e.g., an amount of storage space consumed
by a user or a number of virtualized systems instantiated by the
user). A user can access any of the resources that reside in the
cloud at any time, and from anywhere across the Internet. In
context of the present disclosure, the position map analysis
component 115 could be deployed on a node located in the cloud, and
could receive a position map data structure 120 and signal strength
information for a plurality of access points within a remote WLAN.
The position map analysis component 115 could then determine a
position for one of the access points within the position map is
incorrect, based on the received signal strength information and
the positions of the access points within the position map. Doing
so allows users to access the position map analysis component 115
from any computing system connected to the cloud computing
environment (e.g., via the Internet).
[0047] While the previous discussion is directed to embodiments of
the present disclosure, other and further embodiments of the
disclosure may be devised without departing from the basic scope
thereof. For example, aspects of the present disclosure may be
implemented in hardware or software or in a combination of hardware
and software. One embodiment of the disclosure may be implemented
as a program product for use with a computer system. The program(s)
of the program product define functions of the embodiments
(including the methods described herein) and can be contained on a
variety of computer-readable storage media. Illustrative
computer-readable storage media include, but are not limited to:
(i) non-writable storage media (e.g., read-only memory devices
within a computer such as CD-ROM disks readable by a CD-ROM drive,
flash memory, ROM chips or any type of solid-state non-volatile
semiconductor memory) on which information is permanently stored;
and (ii) writable storage media (e.g., floppy disks within a
diskette drive or hard-disk drive or any type of solid-state
random-access semiconductor memory) on which alterable information
is stored. Such computer-readable storage media, when carrying
computer-readable instructions that direct the functions of the
present disclosure, are embodiments of the present disclosure.
[0048] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality and operation of possible
implementations of systems, methods and computer program products
according to various embodiments. In this regard, each block in the
flowchart or block diagrams may represent a module, segment or
portion of code, which comprises one or more executable
instructions for implementing the specified logical function(s). It
should also be noted that, in some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts, or combinations of special
purpose hardware and computer instructions.
[0049] In view of the foregoing, the scope of the present
disclosure is determined by the claims that follow.
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